如何利用JupyterNotekook做初步分析-创新互联
这篇文章主要介绍如何利用Jupyter Notekook做初步分析,文中介绍的非常详细,具有一定的参考价值,感兴趣的小伙伴们一定要看完!
专注于为中小企业提供成都做网站、成都网站建设、成都外贸网站建设服务,电脑端+手机端+微信端的三站合一,更高效的管理,为中小企业辛集免费做网站提供优质的服务。我们立足成都,凝聚了一批互联网行业人才,有力地推动了成百上千家企业的稳健成长,帮助中小企业通过网站建设实现规模扩充和转变。最近一段时间都是Jupyter Notebook做策略的最初版本设计,就是行情导入画图一类。
之前做个dataframe做分析容易,这个算是简化版本。
新建一个DataAnalyzer 类,这个简单很多,支持从csv和mongodb导入行情数据,和从1分钟k线整合不同分钟k线
下面是导入1分钟螺纹钢数据,整合为5分钟K线
from pymongo import MongoClient, ASCENDING import pandas as pd import numpy as np from datetime import datetime import talib import matplotlib.pyplot as plt import scipy.stats as st %matplotlib inline %config InlineBackend.figure_format = 'retina' class DataAnalyzer(object): """ """ def __init__(self, exportpath="C:\Project\\", datformat=['datetime', 'high', 'low', 'open', 'close','volume']): self.mongohost = None self.mongoport = None self.db = None self.collection = None self.df = pd.DataFrame() self.exportpath = exportpath self.datformat = datformat self.startBar = 2 self.endBar = 12 self.step = 2 self.pValue = 0.015 def db2df(self, db, collection, start, end, mongohost="localhost", mongoport=27017, export2csv=False): """读取MongoDB数据库行情记录,输出到Dataframe中""" self.mongohost = mongohost self.mongoport = mongoport self.db = db self.collection = collection dbClient = MongoClient(self.mongohost, self.mongoport, connectTimeoutMS=500) db = dbClient[self.db] cursor = db[self.collection].find({'datetime':{'$gte':start, '$lt':end}}).sort("datetime",ASCENDING) self.df = pd.DataFrame(list(cursor)) self.df = self.df[self.datformat] self.df = self.df.reset_index(drop=True) path = self.exportpath + self.collection + ".csv" if export2csv == True: self.df.to_csv(path, index=True, header=True) return self.df def csv2df(self, csvpath, dataname="csv_data", export2csv=False): """读取csv行情数据,输入到Dataframe中""" csv_df = pd.read_csv(csvpath) self.df = csv_df[self.datformat] self.df["datetime"] = pd.to_datetime(self.df['datetime']) # self.df["high"] = self.df['high'].astype(float) # self.df["low"] = self.df['low'].astype(float) # self.df["open"] = self.df['open'].astype(float) # self.df["close"] = self.df['close'].astype(float) # self.df["volume"] = self.df['volume'].astype(int) self.df = self.df.reset_index(drop=True) path = self.exportpath + dataname + ".csv" if export2csv == True: self.df.to_csv(path, index=True, header=True) return self.df def df2Barmin(self, inputdf, barmins, crossmin=1, export2csv=False): """输入分钟k线dataframe数据,合并多多种数据,例如三分钟/5分钟等,如果开始时间是9点1分,crossmin = 0;如果是9点0分,crossmin为1""" dfbarmin = pd.DataFrame() highBarMin = 0 lowBarMin = 0 openBarMin = 0 volumeBarmin = 0 datetime = 0 for i in range(0, len(inputdf) - 1): bar = inputdf.iloc[i, :].to_dict() if openBarMin == 0: openBarmin = bar["open"] if highBarMin == 0: highBarMin = bar["high"] else: highBarMin = max(bar["high"], highBarMin) if lowBarMin == 0: lowBarMin = bar["low"] else: lowBarMin = min(bar["low"], lowBarMin) closeBarMin = bar["close"] datetime = bar["datetime"] volumeBarmin += int(bar["volume"]) # X分钟已经走完 if not (bar["datetime"].minute + crossmin) % barmins: # 可以用X整除 # 生成上一X分钟K线的时间戳 barMin = {'datetime': datetime, 'high': highBarMin, 'low': lowBarMin, 'open': openBarmin, 'close': closeBarMin, 'volume' : volumeBarmin} dfbarmin = dfbarmin.append(barMin, ignore_index=True) highBarMin = 0 lowBarMin = 0 openBarMin = 0 volumeBarmin = 0 if export2csv == True: dfbarmin.to_csv(self.exportpath + "bar" + str(barmins)+ str(self.collection) + ".csv", index=True, header=True) return dfbarmin exportpath = "C:\\Project\\" DA = DataAnalyzer(exportpath) #数据库导入 start = datetime.strptime("20190920", '%Y%m%d') end = datetime.now() dfrb8888 = DA.db2df(db="VnTrader_1Min_Db", collection="rb8888", start = start, end = end,export2csv=True) dfrb5min = DA.df2Barmin(dfrb8888,5,crossmin=1, export2csv=True) dfrb5min.tail()
2. 计算5分钟K线的参照,包括标准差,rsi,5分钟均线,和40分钟均线
logdata = pd.DataFrame() logdata['close'] =(dfrb5min['close']) # logdata['tr'] = talib.ATR(np.array(dfrb8888['high']), np.array(dfrb8888['low']), np.array(dfrb8888['close']) ,1) # logdata['atr'] = talib.ATR(np.array(dfrb8888['high']), np.array(dfrb8888['low']), np.array(dfrb8888['close']) ,20) logdata['std20'] = talib.STDDEV( np.array(dfrb5min['close']) ,20) logdata['rsi30'] = talib.RSI(np.array(dfrb5min['close']) ,30) logdata['sma5'] = talib.SMA(np.array(dfrb5min['close']) ,5) logdata['sma40'] = talib.SMA(np.array(dfrb5min['close']) ,40) logdata.plot(subplots=True,figsize=(18,16))
3. 使用快慢均线策略,显示买入卖出点
closeArray = np.array(logdata['close']) listup,listdown = [],[] for i in range(1,len(logdata['close'])): if logdata.loc[i,'sma5'] > logdata.loc[i,'sma40'] and logdata.loc[i-1,'sma5'] < logdata.loc[i-1,'sma40']: listup.append(i) elif logdata.loc[i,'sma5'] < logdata.loc[i,'sma40'] and logdata.loc[i-1,'sma5'] > logdata.loc[i-1,'sma40']: listdown.append(i) fig=plt.figure(figsize=(18,6)) plt.plot(closeArray, color='y', lw=2.) plt.plot(closeArray, '^', markersize=5, color='r', label='UP signal', markevery=listup) plt.plot(closeArray, 'v', markersize=5, color='g', label='DOWN signal', markevery=listdown) plt.legend() plt.show()
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